Kullback-Leibler information as a basis for strong inference in ecological studies

Kenneth P. Burnham and David R. Anderson

Wildlife Research
28(2) 111 - 119
Published: 2001

Abstract

We describe an information-theoretic paradigm for analysis of ecological data,
based on Kullback–Leibler information, that is an extension of
likelihood theory and avoids the pitfalls of null hypothesis testing.
Information-theoretic approaches emphasise a deliberate focus on the
a priori science in developing a set of multiple working
hypotheses or models. Simple methods then allow these hypotheses (models) to
be ranked from best to worst and scaled to reflect a strength of evidence
using the likelihood of each model
(gi), given the data and the
models in the set (i.e.
L(gi
| data)). In addition, a variance component due
to model-selection uncertainty is included in estimates of precision. There
are many cases where formal inference can be based on all the models in the
a priori set and this multi-model inference represents a
powerful, new approach to valid inference. Finally, we strongly recommend
inferences based on a priori considerations be carefully
separated from those resulting from some form of data dredging. An example is
given for questions related to age- and sex-dependent rates of tag loss in
elephant seals (Mirounga leonina).